Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and improve detection systems for fraud and financial crime using data analysis.
- Company: Join a high-growth fintech revolutionising financial crime detection.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and system performance.
- Why this job: Make a real impact by preventing financial crime before it happens.
- Qualifications: Experience in fraud or AML, strong SQL and Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
A high-growth fintech is building out its financial crime product capability across fraud, transaction monitoring, and screening. These roles sit within product and engineering, in the first line. You’ll either be defining how detection works or building the systems it runs on.
What you’ll be doing:
- Building and improving detection systems across fraud and AML (card, banking, TM, screening)
- Working directly with transaction, auth, behavioural, and network data to identify patterns and signals
- Developing detection logic across rules, models, and AI
- Tuning systems continuously: false positives, detection coverage, operational load, customer impact
- Designing features and intelligence layers that improve how risk is detected
- Running deep analysis in SQL and Python, with no reliance on dashboards
- Translating typologies into production-ready signals and decisioning logic
- Deploying and scaling models and rules in real-time systems
- Partnering closely with product, engineering, and compliance to evolve detection frameworks
Where this role can sit:
- Fraud Risk (Card / Banking): CNP, ATO, scams, APP, mule detection, onboarding abuse
- Transaction Monitoring (AML): Owning system performance, rule effectiveness, typology coverage, backlog, false positives
- Screening (Sanctions / PEP): Match quality, list coverage, tuning logic, global screening performance
- Detection Engineering / ML Systems: Building monitoring frameworks from scratch, deploying models into production, scaling decisioning systems across large transaction volumes
What we’re looking for:
- Hands-on experience in fraud, AML, or financial crime risk
- Strong understanding of typologies and how they translate into detection logic
- Strong SQL skills: complex queries, large datasets, no hand-holding
- Python for analysis and modelling (pandas, numpy; ML exposure expected)
- Experience building, tuning, or deploying detection systems (rules and/or models)
- Ability to think about systems end-to-end: not just models, but performance and outcomes
- Comfortable working across product, engineering, and compliance
Profiles that tend to work:
- Fraud/risk data scientists from issuers, fintechs, or banks
- TM or screening specialists who understand system performance, not just policy
- Engineers who’ve built risk or monitoring systems at scale
- Investigators or law-side profiles who’ve moved into detection and pattern analysis
What doesn’t work:
- Ops-only or case handling backgrounds
- Compliance or policy profiles without data or system ownership
- Engineers with no exposure to financial crime or risk systems
- People who can’t show what they’ve built, tuned, or improved
Why this role exists: Most teams measure financial crime after it happens. This team is building the systems that detect it earlier, adapt faster, and scale properly.
Financial Crime Product - Data Scientists & Engineers in London employer: Alexander Barnes
Contact Detail:
Alexander Barnes Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Financial Crime Product - Data Scientists & Engineers in London
✨Tip Number 1
Network like a pro! Reach out to folks in the fintech space, especially those working on financial crime products. Attend industry events or webinars, and don’t be shy about sliding into DMs on LinkedIn. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! If you’ve got experience with SQL and Python, create a portfolio showcasing your projects. Whether it’s a GitHub repo or a personal website, let us see how you’ve tackled real-world problems. This is your chance to shine and demonstrate your hands-on experience!
✨Tip Number 3
Prepare for those interviews by brushing up on your knowledge of fraud detection and AML systems. Be ready to discuss specific examples of how you’ve built or improved detection systems. We want to hear about your thought process and how you approach problem-solving in this field.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to engage directly with us. So, get your application in and let’s make some waves in the fintech world together!
We think you need these skills to ace Financial Crime Product - Data Scientists & Engineers in London
Some tips for your application 🫡
Show Your Passion for Financial Crime: When writing your application, let your enthusiasm for tackling financial crime shine through. We want to see how your experience aligns with our mission to detect and prevent fraud and AML effectively.
Highlight Relevant Skills: Make sure to emphasise your hands-on experience with SQL and Python, especially in relation to building and tuning detection systems. We’re looking for specific examples of how you’ve used these skills in real-world scenarios.
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific requirements of the role. Mention any relevant projects or experiences that demonstrate your understanding of financial crime risk and detection logic.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Alexander Barnes
✨Know Your Financial Crime Stuff
Make sure you brush up on your knowledge of financial crime, especially fraud and AML. Understand the key typologies and how they translate into detection logic. Being able to discuss specific examples from your experience will show that you’re not just familiar with the concepts but can apply them in real-world scenarios.
✨Show Off Your SQL and Python Skills
Since the role requires strong SQL and Python skills, be prepared to demonstrate your expertise. Practice writing complex queries and performing data analysis using Python libraries like pandas and numpy. You might even get asked to solve a problem on the spot, so being comfortable with these tools is crucial.
✨Think End-to-End
During the interview, highlight your ability to think about systems holistically. Discuss how you’ve built, tuned, or deployed detection systems in the past, focusing on performance and outcomes. This shows that you understand the bigger picture and can contribute to the team’s goal of detecting financial crime earlier.
✨Collaborate Like a Pro
This role involves working closely with product, engineering, and compliance teams. Be ready to share examples of how you’ve successfully collaborated across different departments in previous roles. Emphasising your teamwork skills will demonstrate that you can thrive in a cross-functional environment.